
"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A     :			int  						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A     :			Optional[Any]  						=			[]
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
   )
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
   )
   rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
   )
   rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
   rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
   )
   rename_keys.append(
       (
           F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
           F"decoder.layers.{i}.encoder_attn.out_proj.weight",
       )
   )
   rename_keys.append(
       (
           F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
           F"decoder.layers.{i}.encoder_attn.out_proj.bias",
       )
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
   )

   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			int	,					__snake_case							:			Optional[int]    )					->							Optional[int]:
       lowercase       :							Dict									=				state_dict.pop(UpperCamelCase_    )
       lowercase       :							int									=				val


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Tuple:
       lowercase       :							Any									=				OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase       :							Tuple									=				key.replace("backbone.0.body"	,					"backbone.conv_encoder.model"    )
                     lowercase       :							Optional[int]									=				value
              else:
                     lowercase       :							Tuple									=				value

       return new_state_dict


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			Tuple=False    )					->							List[str]:
       lowercase       :							int									=				""
       if is_panoptic:
              lowercase       :							int									=				"conditional_detr."

       # first: transformer encoder
       for i in range(6    ):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              lowercase       :							int									=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""    )
              lowercase       :							Tuple									=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""    )
              # next, add query, keys and values (in that order) to the state dict
              lowercase       :							int									=				in_proj_weight[:256, :]
              lowercase       :							List[Any]									=				in_proj_bias[:256]
              lowercase       :							int									=				in_proj_weight[256:512, :]
              lowercase       :							Any									=				in_proj_bias[256:512]
              lowercase       :							Optional[Any]									=				in_proj_weight[-256:, :]
              lowercase       :							Tuple									=				in_proj_bias[-256:]


def     __magic_name__      ( )					->							Tuple:
       lowercase       :							Optional[int]									=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase       :							int									=				Image.open(requests.get(UpperCamelCase_	,					stream=UpperCamelCase_    ).raw    )

       return im


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			Union[str, Any]    )					->							int:
       lowercase       :							str									=				ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowercase       :							Dict									=				"resnet101"
       if "dc5" in model_name:
              lowercase       :							Any									=				True
       lowercase       :							Optional[int]									=				"panoptic" in model_name
       if is_panoptic:
              lowercase       :							int									=				250
       else:
              lowercase       :							Tuple									=				91
              lowercase       :							Tuple									=				"huggingface/label-files"
              lowercase       :							Any									=				"coco-detection-id2label.json"
              lowercase       :							Tuple									=				json.load(open(hf_hub_download(UpperCamelCase_	,					UpperCamelCase_	,					repo_type="dataset"    )	,					"r"    )    )
              lowercase       :							Any									=				{int(UpperCamelCase_    ): v for k, v in idalabel.items()}
              lowercase       :							int									=				idalabel
              lowercase       :							Dict									=				{v: k for k, v in idalabel.items()}

       # load image processor
       lowercase       :							Optional[int]									=				"coco_panoptic" if is_panoptic else "coco_detection"
       lowercase       :							int									=				ConditionalDetrImageProcessor(format=UpperCamelCase_    )

       # prepare image
       lowercase       :							Union[str, Any]									=				prepare_img()
       lowercase       :							Optional[int]									=				image_processor(images=UpperCamelCase_	,					return_tensors="pt"    )
       lowercase       :							List[Any]									=				encoding["pixel_values"]

       logger.info(f"""Converting model {model_name}..."""    )

       # load original model from torch hub
       lowercase       :							Optional[int]									=				torch.hub.load("DeppMeng/ConditionalDETR"	,					UpperCamelCase_	,					pretrained=UpperCamelCase_    ).eval()
       lowercase       :							str									=				conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase       :							Optional[Any]									=				"conditional_detr." + src
              rename_key(UpperCamelCase_	,					UpperCamelCase_	,					UpperCamelCase_    )
       lowercase       :							Tuple									=				rename_backbone_keys(UpperCamelCase_    )
       # query, key and value matrices need special treatment
       read_in_q_k_v(UpperCamelCase_	,					is_panoptic=UpperCamelCase_    )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase       :							Dict									=				"conditional_detr.model." if is_panoptic else "model."
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("conditional_detr"    )
                         and not key.startswith("class_labels_classifier"    )
                         and not key.startswith("bbox_predictor"    )
                     ):
                            lowercase       :							Optional[int]									=				state_dict.pop(UpperCamelCase_    )
                            lowercase       :							int									=				val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase       :							Any									=				state_dict.pop(UpperCamelCase_    )
                            lowercase       :							Dict									=				val
                     elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
                            continue
                     else:
                            lowercase       :							List[Any]									=				state_dict.pop(UpperCamelCase_    )
                            lowercase       :							Optional[Any]									=				val
              else:
                     if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
                            lowercase       :							Dict									=				state_dict.pop(UpperCamelCase_    )
                            lowercase       :							Tuple									=				val
    # finally, create HuggingFace model and load state dict
       lowercase       :							int									=				ConditionalDetrForSegmentation(UpperCamelCase_    ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_    )
       model.load_state_dict(UpperCamelCase_    )
       model.eval()
       model.push_to_hub(repo_id=UpperCamelCase_	,					organization="DepuMeng"	,					commit_message="Add model"    )
       # verify our conversion
       lowercase       :							List[str]									=				conditional_detr(UpperCamelCase_    )
       lowercase       :							Union[str, Any]									=				model(UpperCamelCase_    )
       assert torch.allclose(outputs.logits	,					original_outputs["pred_logits"]	,					atol=1E-4    )
       assert torch.allclose(outputs.pred_boxes	,					original_outputs["pred_boxes"]	,					atol=1E-4    )
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks	,					original_outputs["pred_masks"]	,					atol=1E-4    )

       # Save model and image processor
       logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""    )
       Path(UpperCamelCase_    ).mkdir(exist_ok=UpperCamelCase_    )
       model.save_pretrained(UpperCamelCase_    )
       image_processor.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
   _A     :			Any  						=			argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""",
       default="""conditional_detr_resnet50""",
       type=str,
       help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   _A     :			Tuple  						=			parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




